Review of guidelines for good practice in decision-analytic modelling in health technology assessment
Z Philips,1 L Ginnelly,1* M Sculpher,1 K Claxton,1,2 S Golder,3 R Riemsma,3 N Woolacott3 and J Glanville3
1 Centre for Health Economics,
2 Department of Economics,
3 Centre for Reviews and Dissemination,
Health Technology Assessment 2004; Vol. 8: No. 36
Available online PDF [179p.] at: http://www.hta.ac.uk/fullmono/mon836.pdf
Objectives: To identify existing guidelines and develop a synthesised guideline plus accompanying checklist. In addition to provide guidance on key theoretical, methodological and practical issues and consider the implications of this research for what might be expected of future decision-analytic models.
Data sources: Electronic databases.
Review methods: A systematic review of existing good practice guidelines was undertaken to identify and summarise guidelines currently available for assessing
the quality of decision-analytic models that have been undertaken for health technology assessment. A synthesised good practice guidance and accompanying
checklist was developed. Two specific methods areas in decision modelling were considered. The first method’s topic is the identification of parameter estimates from published literature. Parameter searches were developed and piloted using a case-study model.
The second topic relates to bias in parameter estimates; that is, how to adjust estimates of treatment effect from observational studies where there are risks of selection bias. A systematic literature review was conducted to identify those studies looking at quantification of bias in parameter estimates and the implication of this bias.
Content:
Executive summary
1 Introduction
2 A framework for good practice in decision analytic modelling studies: review of current guidelines
3 Development of a best practice guideline
Developing the synthesised guidance
Statements of good practice
Applying the guidance: the use of case studies
4 Key methodological and practical issues not covered in existing published guidelines
Appropriate methods for the identification and quality assessment of secondary parameter estimates such as utilities, costs, incidence and prevalence
Effects of selection bias on treatment outcomes
5 Discussion
Review of guidelines for quality assessment in decision models
Methods in decision modelling
Implications for the NICE technology assessment and appraisal process
Recommendations for research
References
Appendix 1 Search strategies for guidelines of good practice in decision modelling
Appendix 2 Summary of guidelines available in structured format
Appendix 3 Quality assessment in decision-analytic models: a suggested checklist
Appendix 4 Application of the checklist to three decision models
Appendix 5 Members of the Expert
Appendix 6 Comments from the Expert Advisory Group and amended checklist
Appendix 7 Bibliography
Appendix 8 Search strategies and results of the case study
Appendix 9 Searches for the effects
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